2023 Incubator Presentations: Will Kumler & Bernease Herman

2023 Incubator Presentations: Will Kumler & Bernease Herman


4:30 pm – 5:30 pm

Please join us for a UW Data Science Seminar event on Wednesday, March 29th from 4:30 to 5:20 p.m. PST. The seminar will feature two projects from our 2023 Data Science Incubator program: Will Kumler, a Graduate Student from the UW School of Oceanography; and Bernease Herman, eScience Institute Data Scientist.

Use this zoom link to join


“Constructing a robust metric of peak quality for untargeted mass-spectrometry”

Abstract: Mass spectrometry is a cutting-edge analysis field used to identify the molecular composition of samples taken from medical laboratories, the depths of the ocean, and even outer space. In the Ingalls Lab at UW, we use it to characterize the molecular composition of seawater and its inhabitants, a task complicated by the complex biogeochemistry of the oceans. The nascent nature of modern mass spectrometry also introduces many challenges, one of which is distinguishing biological/chemical signal from noise produced during the measurement process. My goal is to calibrate existing detection algorithms to a probabilistic likelihood that the signal corresponds to a real molecular feature. This will involve estimating the relative strength of various metrics used for detecting molecules, using machine learning methods to construct the probabilistic estimate, and ideally constructing packages that interface with existing software to facilitate widespread adoption.

Link to full Incubator project here.


“Investigating Structure of Social Science Research Datasets for Better ML Evaluation”

Abstract: Specialized machine learning architectures, such as deep learning, typically rely on inductive biases and other data-specific correlational structure information to produce more effective models. Similarly, the design and evaluation of differentially private synthesizers depends heavily on the correlational structure of the datasets most commonly used in the field. We wish to investigate differences in the correlational structure of popular machine learning benchmark datasets with those of other disciplines who utilize machine learning, starting with social science data. We will both investigate the structure by repurposing existing descriptive dataset metrics in addition to exploring new graph-based metrics that generalize well across many data types.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

The 2022-2023 seminars will be virtual, and are free and open to the public.